Model Overview

We estimate the age of a site by calculating the years since the last fire. We then fit a curve to model the recovery of vegetation (measured using NDVI) as a function of it’s age. An additional level models the parameters of the negative exponential curve as a function of environmental variables. This means that sites with similar environmental conditions should have similar recovery curves.

Input data

The model was last fit on 2022-03-17 13:19:55.

This version of the model was fit with 1076106 pixels including data from 2002-04-23 to 2019-12-19.

Workflow

This repository was developed using the Targets framework.

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ── ✔ ggplot2 3.3.5 ✔ purrr 0.3.4 ✔ tibble 3.1.6 ✔ dplyr 1.0.8 ✔ tidyr 1.2.0 ✔ stringr 1.4.0 ✔ readr 2.1.2 ✔ forcats 0.5.1 ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── ✖ dplyr::filter() masks stats::filter() ✖ dplyr::lag() masks stats::lag()

Attaching package: ‘arrow’

The following object is masked from ‘package:utils’:

timestamp

CmdStan path set to: /home/rstudio/.cmdstanr/cmdstan-2.29.1

Results

Environmental Associations with Ecosystem Recovery

These parameters represent the relationship of the following environmental variables to the recovery trajectory.

Recovery Trajectories

The plot below illustrates some example recovery trajectories. It currently just shows the top 20 cells with the most observations.

Model Performance

Compare estimated vs observed values for all pixels. This is not true validation - these pixels were included in the model fitting.

model_prediction %>% 
#  filter(cellID%in%cells_with_long_records$cellID) %>% 
  ggplot(aes(x=median,y=y_obs)) +
  geom_hex(bins=50)+
  geom_smooth(method = "lm",col="red")+
  geom_abline(color="blue")+
#  facet_wrap(~cellID) +
  scale_fill_viridis_c()+
  labs(x="Estimated NDVI",y="Observed NDVI",
       caption = "Blue line is 1:1, Red line is least squares regression. Count is the number of pixels in that location",
       title = "Estimated vs. Observed NDVI"
      ) +
  theme_bw()

## Warning: Computation failed in `stat_binhex()`:

## `geom_smooth()` using formula 'y ~ x'


## Spatial Predictions

Maps of spatial parameters in the model.  


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